{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "using Knet\n",
    "include(\"dcgan.jl\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = DCGAN.sample_noise(Knet.KnetArray{Float32},100,64);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "┌ Info: Loading MNIST...\n",
      "└ @ Main.DCGAN /kuacc/users/dyuret/.julia/dev/Knet/data/mnist.jl:33\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training started...\n"
     ]
    }
   ],
   "source": [
    "wd, wg, md, mg = DCGAN.main(\"--epochs 0 --seed 1\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "233×233 reinterpret(Gray{Float64}, ::Array{Float64,2}):\n",
       " Gray{Float64}(1.0)  Gray{Float64}(1.0)       Gray{Float64}(1.0)       …  Gray{Float64}(1.0)       Gray{Float64}(1.0)       Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)     Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)     Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)     Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)     Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  …  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)     Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)     Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500949)     Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500947)     Gray{Float64}(0.500949)  Gray{Float64}(0.500949)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500949)  …  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)     Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500949)     Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " ⋮                                                                     ⋱  ⋮                                                                   \n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500949)     Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)     Gray{Float64}(0.500947)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500947)  Gray{Float64}(0.500948)     Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)     Gray{Float64}(0.500947)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  …  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)     Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)     Gray{Float64}(0.500948)  Gray{Float64}(0.500949)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)     Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)     Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  …  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.500948)  Gray{Float64}(0.500948)     Gray{Float64}(0.500948)  Gray{Float64}(0.500948)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(1.0)       Gray{Float64}(1.0)          Gray{Float64}(1.0)       Gray{Float64}(1.0)       Gray{Float64}(1.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DCGAN.plot_generations(wg, mg; z=z);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training started...\n",
      " 33.151022 seconds (28.80 M allocations: 1.691 GiB, 15.40% gc time)\n",
      "(:epoch, 1, :dloss, 1.0359865f0, :gloss, 1.0456004f0)\n",
      " 13.092222 seconds (7.92 M allocations: 655.951 MiB, 1.06% gc time)\n",
      "(:epoch, 2, :dloss, 0.9258089f0, :gloss, 1.2359008f0)\n",
      " 13.093152 seconds (7.94 M allocations: 656.175 MiB, 1.09% gc time)\n",
      "(:epoch, 3, :dloss, 0.8401469f0, :gloss, 1.379799f0)\n",
      " 13.203502 seconds (7.94 M allocations: 656.139 MiB, 1.02% gc time)\n",
      "(:epoch, 4, :dloss, 0.78719443f0, :gloss, 1.5027276f0)\n",
      " 13.091020 seconds (7.94 M allocations: 656.041 MiB, 1.07% gc time)\n",
      "(:epoch, 5, :dloss, 0.73330337f0, :gloss, 1.6014324f0)\n",
      " 13.391387 seconds (7.94 M allocations: 656.151 MiB, 1.25% gc time)\n",
      "(:epoch, 6, :dloss, 0.70610285f0, :gloss, 1.6730818f0)\n",
      " 13.348711 seconds (7.94 M allocations: 655.935 MiB, 1.41% gc time)\n",
      "(:epoch, 7, :dloss, 0.67186964f0, :gloss, 1.7409074f0)\n",
      " 13.357577 seconds (7.94 M allocations: 656.120 MiB, 1.40% gc time)\n",
      "(:epoch, 8, :dloss, 0.6605091f0, :gloss, 1.8058909f0)\n",
      " 13.342279 seconds (7.94 M allocations: 656.024 MiB, 1.38% gc time)\n",
      "(:epoch, 9, :dloss, 0.64583325f0, :gloss, 1.83894f0)\n",
      " 13.282196 seconds (7.94 M allocations: 656.177 MiB, 1.22% gc time)\n",
      "(:epoch, 10, :dloss, 0.63358414f0, :gloss, 1.9018079f0)\n"
     ]
    }
   ],
   "source": [
    "wd, wg, md, mg = DCGAN.main(\"--epochs 10 --seed 1\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "233×233 reinterpret(Gray{Float64}, ::Array{Float64,2}):\n",
       " Gray{Float64}(1.0)  Gray{Float64}(1.0)         Gray{Float64}(1.0)          …  Gray{Float64}(1.0)         Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.112131)    Gray{Float64}(0.0607674)       Gray{Float64}(0.128719)    Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.0570908)   Gray{Float64}(0.0268157)       Gray{Float64}(0.0729916)   Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.0156606)   Gray{Float64}(0.00387567)      Gray{Float64}(0.0231529)   Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.00796762)  Gray{Float64}(0.00143993)      Gray{Float64}(0.0166104)   Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.00403592)  Gray{Float64}(0.000594288)  …  Gray{Float64}(0.00898179)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.00593236)  Gray{Float64}(0.00176623)      Gray{Float64}(0.0121648)   Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.00521025)  Gray{Float64}(0.00103688)      Gray{Float64}(0.0147743)   Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.00570032)  Gray{Float64}(0.00169846)      Gray{Float64}(0.0255868)   Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.00287673)  Gray{Float64}(0.000282228)     Gray{Float64}(0.0160781)   Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.00246871)  Gray{Float64}(0.000356585)  …  Gray{Float64}(0.0191758)   Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.00173229)  Gray{Float64}(0.000232637)     Gray{Float64}(0.0222301)   Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.0014326)   Gray{Float64}(0.000199378)     Gray{Float64}(0.0126501)   Gray{Float64}(1.0)\n",
       " ⋮                                                                          ⋱                                               \n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.00838754)  Gray{Float64}(0.00114551)      Gray{Float64}(0.00311816)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.00828069)  Gray{Float64}(0.00111997)      Gray{Float64}(0.00211295)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.00782621)  Gray{Float64}(0.000884593)     Gray{Float64}(0.00252092)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.00415558)  Gray{Float64}(0.000114173)     Gray{Float64}(0.0018307)   Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.00330788)  Gray{Float64}(8.94368e-5)   …  Gray{Float64}(0.00254136)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.00465375)  Gray{Float64}(0.00045675)      Gray{Float64}(0.00321242)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.00729901)  Gray{Float64}(0.00157252)      Gray{Float64}(0.00516024)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.018855)    Gray{Float64}(0.00463274)      Gray{Float64}(0.00847745)  Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.0254191)   Gray{Float64}(0.00377965)      Gray{Float64}(0.0155607)   Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.0681637)   Gray{Float64}(0.0165324)    …  Gray{Float64}(0.0354999)   Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(0.169676)    Gray{Float64}(0.105701)        Gray{Float64}(0.116786)    Gray{Float64}(1.0)\n",
       " Gray{Float64}(1.0)  Gray{Float64}(1.0)         Gray{Float64}(1.0)             Gray{Float64}(1.0)         Gray{Float64}(1.0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "DCGAN.plot_generations(wg, mg; z=z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Julia 1.0.0",
   "language": "julia",
   "name": "julia-1.0"
  },
  "language_info": {
   "file_extension": ".jl",
   "mimetype": "application/julia",
   "name": "julia",
   "version": "1.0.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
